Parkinson disease detection using energy direction features based on EMD from voice signal

• Autorzy: Zhang Tao , Zhang Yajuan , Sun Hao , Shan Haoran

Identyfikatory

DOI

10.1016/j.bbe.2020.12.009

• Języki publikacji: EN

Abstrakty

EN

Voice disorders are one of the incipient symptoms of Parkinson's disease (PD). Recent studies have shown that approximately 90% of PD patients suffer from vocal disorders. Therefore, it is significant to extract pathological information on the voice signals to detect PD. In this paper, a feature, named energy direction features based on empirical mode decomposition (EDF-EMD), is proposed to show the different characteristics of voice signals between PD patients and healthy subjects. Firstly, the intrinsic mode functions (IMFs) were obtained through the decomposition of voice signals by EMD. Then, the EDF is obtained by calculating the directional derivatives of the energy spectrum of each IMFs. Finally, the performance of the proposed feature is verified on two different datasets: dataset-Sakar and dataset-CPPDD. The proposed approach shows the best average resulting accuracy of 96.54% on dataset-Sakar and 92.59% on dataset-CPPDD. The results demonstrate that the method proposed in this paper is promising in the field of PD detection.

Słowa kluczowe

EN

Parkinson's disease; PD; energy direction feature; empirical mode decomposition; EDF-EMD; energy spectrum; vocal disorders; voice signals

PL

choroba Parkinsona; PD; empiryczna dekompozycja sygnału; EDF-EMD; widmo energii

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 1
• Strony: 127--141
• Opis fizyczny: Bibliogr. 58 poz., rys., tab., wykr.

Twórcy

autor

Zhang Tao

• School of Information Science and Engineering, Yanshan University, Hebei Street, No438, Qinhuangdao 066004, PR China; Hebei Key Laboratory on Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, PR China

autor

Zhang Yajuan

• School of Information Science and Engineering, Yanshan University, Qinhuangdao, PR China; Hebei Key Laboratory on Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, PR China

autor

Sun Hao

• School of Information Science and Engineering, Yanshan University, Qinhuangdao, PR China; Hebei Key Laboratory on Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, PR China

autor

Shan Haoran

• School of Information Science and Engineering, Yanshan University, Qinhuangdao, PR China; Hebei Key Laboratory on Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, PR China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).